AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data
Summary: AutoTSAD unsupervisedly ensembles diverse subsequence detectors to produce anomaly scores for time series without training data. Automated configuration and ensembling match tuned detectors and beat prior selection baselines across diverse anomaly types. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,738 | TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 4.1945683e-05 |
| 10,830 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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